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A biophysical observation model for field potentials of networks of leaky integrate-and-fire neurons.

Beim Graben P, Rodrigues S - Front Comput Neurosci (2013)

Bottom Line: We present a biophysical approach for the coupling of neural network activity as resulting from proper dipole currents of cortical pyramidal neurons to the electric field in extracellular fluid.This work aligns and satisfies the widespread dipole assumption that is motivated by the "open-field" configuration of the DFP around cortical pyramidal cells.In particular, by means of numerical simulations we compare our approach with an ad hoc model by Mazzoni et al. (2008), and conclude that our biophysically motivated approach yields substantial improvement.

View Article: PubMed Central - PubMed

Affiliation: Bernstein Center for Computational Neuroscience Berlin Berlin, Germany ; Department of German Language and Linguistics, Humboldt-Universität zu Berlin Berlin, Germany.

ABSTRACT
We present a biophysical approach for the coupling of neural network activity as resulting from proper dipole currents of cortical pyramidal neurons to the electric field in extracellular fluid. Starting from a reduced three-compartment model of a single pyramidal neuron, we derive an observation model for dendritic dipole currents in extracellular space and thereby for the dendritic field potential (DFP) that contributes to the local field potential (LFP) of a neural population. This work aligns and satisfies the widespread dipole assumption that is motivated by the "open-field" configuration of the DFP around cortical pyramidal cells. Our reduced three-compartment scheme allows to derive networks of leaky integrate-and-fire (LIF) models, which facilitates comparison with existing neural network and observation models. In particular, by means of numerical simulations we compare our approach with an ad hoc model by Mazzoni et al. (2008), and conclude that our biophysically motivated approach yields substantial improvement.

No MeSH data available.


Comparison of power spectra of the various LFP measures when the network receives constant signal with three different rates (1.2, 1.6, and 2.4 spikes/ms). The first panels (A–C) corresponding to the different rates shows the power spectrum of the average membrane potential . The second panels (D–F) and third panels (G–I) show power spectra of the total and average of L1 and L2 corresponding to Mazzoni et al. (2008), respectively. The fourth panels (J–L) and the last panels (M–O) display power spectra of the L3 and L4 measures from our model, respectively. Note we show the full spectrum up to 5 kHz only for convenience due to the fine sample rate.
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Figure 5: Comparison of power spectra of the various LFP measures when the network receives constant signal with three different rates (1.2, 1.6, and 2.4 spikes/ms). The first panels (A–C) corresponding to the different rates shows the power spectrum of the average membrane potential . The second panels (D–F) and third panels (G–I) show power spectra of the total and average of L1 and L2 corresponding to Mazzoni et al. (2008), respectively. The fourth panels (J–L) and the last panels (M–O) display power spectra of the L3 and L4 measures from our model, respectively. Note we show the full spectrum up to 5 kHz only for convenience due to the fine sample rate.

Mentions: In Figure 5 we finally contrast the power spectra of the different LFP measures.


A biophysical observation model for field potentials of networks of leaky integrate-and-fire neurons.

Beim Graben P, Rodrigues S - Front Comput Neurosci (2013)

Comparison of power spectra of the various LFP measures when the network receives constant signal with three different rates (1.2, 1.6, and 2.4 spikes/ms). The first panels (A–C) corresponding to the different rates shows the power spectrum of the average membrane potential . The second panels (D–F) and third panels (G–I) show power spectra of the total and average of L1 and L2 corresponding to Mazzoni et al. (2008), respectively. The fourth panels (J–L) and the last panels (M–O) display power spectra of the L3 and L4 measures from our model, respectively. Note we show the full spectrum up to 5 kHz only for convenience due to the fine sample rate.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3539751&req=5

Figure 5: Comparison of power spectra of the various LFP measures when the network receives constant signal with three different rates (1.2, 1.6, and 2.4 spikes/ms). The first panels (A–C) corresponding to the different rates shows the power spectrum of the average membrane potential . The second panels (D–F) and third panels (G–I) show power spectra of the total and average of L1 and L2 corresponding to Mazzoni et al. (2008), respectively. The fourth panels (J–L) and the last panels (M–O) display power spectra of the L3 and L4 measures from our model, respectively. Note we show the full spectrum up to 5 kHz only for convenience due to the fine sample rate.
Mentions: In Figure 5 we finally contrast the power spectra of the different LFP measures.

Bottom Line: We present a biophysical approach for the coupling of neural network activity as resulting from proper dipole currents of cortical pyramidal neurons to the electric field in extracellular fluid.This work aligns and satisfies the widespread dipole assumption that is motivated by the "open-field" configuration of the DFP around cortical pyramidal cells.In particular, by means of numerical simulations we compare our approach with an ad hoc model by Mazzoni et al. (2008), and conclude that our biophysically motivated approach yields substantial improvement.

View Article: PubMed Central - PubMed

Affiliation: Bernstein Center for Computational Neuroscience Berlin Berlin, Germany ; Department of German Language and Linguistics, Humboldt-Universität zu Berlin Berlin, Germany.

ABSTRACT
We present a biophysical approach for the coupling of neural network activity as resulting from proper dipole currents of cortical pyramidal neurons to the electric field in extracellular fluid. Starting from a reduced three-compartment model of a single pyramidal neuron, we derive an observation model for dendritic dipole currents in extracellular space and thereby for the dendritic field potential (DFP) that contributes to the local field potential (LFP) of a neural population. This work aligns and satisfies the widespread dipole assumption that is motivated by the "open-field" configuration of the DFP around cortical pyramidal cells. Our reduced three-compartment scheme allows to derive networks of leaky integrate-and-fire (LIF) models, which facilitates comparison with existing neural network and observation models. In particular, by means of numerical simulations we compare our approach with an ad hoc model by Mazzoni et al. (2008), and conclude that our biophysically motivated approach yields substantial improvement.

No MeSH data available.